Medical data contains information that is vital for clinical diagnosis as well as treatment planning. Nowadays, varieties of clinical data are not optimally and comprehensively utilized towards medical decision making. This is because simple human inspection or traditional computational methods are incapable of extracting the hidden patterns contained in the data, which are very important to form recommendations and predictions for both diagnosis and treatment planning. In recent decades, more types and quantities of medical data have been collected due to advanced technology. A large number of significant and critical information is contained in these medical data. Still, highly-efficient, automated computational methods are needed to process and analyze the available medical data, in order to provide the physicians with recommendations and predictions on diagnostic decisions and treatment planning.
Traumatic pelvic injury is a severe yet common injury in the United States, often caused by motor vehicle accidents or fall. Commonly, pelvic injuries are assessed using information contained in the pelvic Computed Tomography (CT) images, making these images important for assessing the severity and prognosis of traumatic pelvic injuries. Each pelvic CT scan includes a large number of slices. Meanwhile, each slice contains a large quantity of data that may not be thoroughly and accurately analyzed via simple visual inspection with the desired accuracy and speed. Hence, a computer-assisted pelvic trauma decision-making system would be valuable to assist physicians in making accurate diagnostic decisions and determining treatment planning in a short period of time. Currently, however, due to factors such as limited resolution of medical images, variations in bone tissues, complexity of pelvic structures, and significantly different geometrical characteristics of fractures, automatic detection and segmentation of pelvic bone and, more valuably, detection of the bone fractures using CT scan imaging remains a significant challenge. Moreover, the existing decision-making systems for such traumatic injuries do not extract and/or consider the features automatically detected from medical images.